Finally, research on synchronic metaphor identification has applied a wide range of approaches, including binary classification relying on standard distributional similarity [1]}, text cohesion measures [2]}, classification relying on abstractness cues [3]}, [4]} or cross-lingual information [5]}, and soft clustering [6]}, among others. As to our knowledge, no previous work has explicitly exploited the idea of generalization (via hypernymy models) in metaphor detection yet.
